Bruno and Darko take a light, conversational look at the long-running “frequentist vs Bayesian” divide. From Bayes’ unpublished notes to Laplace turning the idea into something bigger. A fun episode touching on how people have argued about probability for centuries and why both approaches still show up in today’s thinking.

Bruno and Darko discuss the Fibonacci sequence — from rabbits to the surprising patterns it shaped — before jumping to James Lind’s scurvy experiment, one of history’s first clinical trials. A brief episode tying together math, story, and the roots of evidence-based testing.

Darko is joined by Bruno live from Times Square to discuss AI concepts with the real-world chaos of a crowd. They talk about how unpredictable human movement mirrors the way AI models learn from massive datasets. A fast, grounded conversation linking everyday patterns in Times Square to core ideas in machine learning.

Bruno and Darko unpack how tree-based models branch out in biostatistics and where they root themselves in real oncology data. A quick and clever look at how AI learns to see the forest and the trees at once.

Bruno and Darko discuss randomization and confounders, two core concepts in clinical research. They explore how study design, data quality, and bias control affect the reliability of trial outcomes, and how AI can help identify or adjust for confounding variables. A direct, practical talk on what truly shapes trustworthy data in oncology research.

Bruno and Darko talk about AGI, defining what it is, what current AI systems can actually do, and what remains speculation. They keep the focus on practical implications for oncology and data analysis, separating real capabilities from hype. A straightforward check on where AI stands today and what to expect next.